Data sharing method, data sharing system, electronic equipment and storage medium

Abstract

The invention discloses a data sharing method, a data sharing system, electronic equipment and a computer readable storage medium. Based on the data sharing method, a server side can obtain data features of private data of different data bodies for the same target product and form a virtual data set, and data sharing under privacy protection is achieved through the virtual data set. Therefore, each data main body can roughly know which private data other data main bodies have through the server side. According to the scheme, data barriers among different data main bodies can be reduced while the data privacy of the private data is guaranteed.

Description

Data sharing method, data sharing system, electronic device and storage medium Technical Field

[0001] The present application belongs to the field of data processing technology, and in particular relates to a data sharing method, a data sharing system, an electronic device, and a computer-readable storage medium. Background Art

[0002] In electric vehicles, the battery is a critical component. Due to the complex workings of the battery, multiple stakeholders (such as battery manufacturers, automakers, battery management systems, and vehicle owners) can interact with the battery in different ways, affecting its state.

[0003] However, due to various reasons such as data privacy, it is difficult for any participant to know who the other participants are, and it is even more difficult to know what battery-related data each other participant has, resulting in data barriers between the participants.

[0004] Application Contents

[0005] The purpose of the embodiments of the present application is to provide a data sharing method, a data sharing system, an electronic device, and a computer-readable storage medium that can reduce data barriers between different participants in the same product while protecting data privacy.

[0006] In a first aspect, the present application provides a data sharing method applied to a server, comprising:

[0007] Obtaining data characteristics of private data of at least one data subject in the life cycle of a target product;

[0008] Based on the association between data subjects and data features, data features are integrated into virtual data sets;

[0009] Shared virtual datasets.

[0010] In the technical solutions of the embodiments of the present application, on the one hand, for the private data obtained by a data subject for a target product, the server focuses not on the private data itself, but rather on the data characteristics of that private data. It can be understood that the data characteristics of private data are objective and identifiable summaries of that private data without revealing its privacy, and can, to a certain extent, represent the private data. On the other hand, a target product often involves multiple data subjects throughout its lifecycle. The data characteristics of each data subject's different private data for the target product can be stored in a virtual dataset on the server, thus achieving a closed-loop data structure for the target product's entire lifecycle. To avoid confusion, the virtual dataset can also store the association between the data subject and the data characteristics to clarify the ownership of the private data corresponding to each data characteristic. Based on these two aspects, the server can form a virtual dataset for the entire lifecycle of the target product. Through this virtual dataset, the server can achieve systematic and comprehensive sharing of the data characteristics of each data subject's private data, thereby reducing data barriers between different data subjects while protecting the data privacy of each data subject's private data.

[0011] In some embodiments, obtaining data characteristics of private data of at least one data subject in the life cycle of a target product includes:

[0012] receiving private data of at least one data subject in the life cycle of a target product;

[0013] Generate data features based on private data.

[0014] The data uploaded to the server can be the data subject's private data specific to the target product. Upon receiving this private data, the server can perform a series of subsequent data processing to generate corresponding data features. Through this process, data processing of private data is deployed on the server, thereby fully utilizing the server's high computing power.

[0015] In some embodiments, obtaining data features of private data of a target product includes:

[0016] Based on the preset data feature standard information, the private data is standardized to obtain standardized data;

[0017] Generate data features based on the standardized data.

[0018] To facilitate data sharing among all parties through the server and unify the different expressions of the same data objects by various data subjects, standardization can also be introduced. Through standardization, data from multiple parties adopts the same standards, which can greatly improve the efficiency of multi-party data communication and collaboration.

[0019] In some embodiments, before normalizing the private data based on the preset data feature standard information to obtain the standardized data, the data sharing method further includes:

[0020] Perform data cleaning on private data to obtain cleaned data;

[0021] Accordingly, based on the preset data feature standard information, the private data is standardized to obtain standardized data, including:

[0022] Based on the data feature standard information, the cleaned data is standardized to obtain standardized data.

[0023] To facilitate data sharing among all parties and avoid interference from redundant and / or invalid information, the server can also introduce data cleaning operations. After this data cleaning process, the amount of data that needs to be standardized is reduced, thereby improving the processing efficiency of the standardization process.

[0024] In some embodiments, obtaining data characteristics of private data of at least one data subject in the life cycle of a target product includes:

[0025] Data characteristics of private data of at least one data subject in a life cycle of a target product are received.

[0026] The data uploaded to the server can also be data features of the data subject's private data specific to the target product. This allows the server to directly update the virtual dataset upon receiving these data features, eliminating the need for time-consuming and resource-intensive data processing. This process alleviates data processing pressure on the server, enabling it to maintain normal operation even in high-concurrency scenarios.

[0027] In some embodiments, the data characteristics include a summary and / or a data description of the private data.

[0028] Through the summary of private data, the integrity and reliability of private data can be verified, which can reduce data fraud caused by tampering of private data to a certain extent; through the data description of private data, other data subjects can understand the basic information of the private data, so that other data subjects can accurately judge whether the private data can be used by them based on the data description.

[0029] In some embodiments, the server shares a virtual dataset, including:

[0030] Receive data access requests from data access parties;

[0031] The virtual dataset is sent to the data query party based on the data query request.

[0032] If a target product involves too many data subjects during its lifecycle, the volume and frequency of updates to the virtual dataset will increase significantly. To reduce this burden, the server can adapt to the needs of data subjects and only send virtual datasets to data query parties upon receiving a data query request. This reduces the risk of overly frequent pushes of virtual datasets to the same data subject.

[0033] In some embodiments, the data sharing method further comprises:

[0034] Receive a data negotiation request from a data requester to obtain target private data;

[0035] Determine the data owner of the target private data;

[0036] Send a data negotiation request to the data owner.

[0037] By sharing data based on data characteristics, the server can reduce data barriers between different data subjects, allowing data subjects to access the data characteristics of each other data subject's private data, thereby gaining a general understanding of the private data held by each other data subject. Because the server is a reliable third party, if a data subject truly needs to use the private data of another data subject, it can initiate a data negotiation request with the data owner of the target private data through the server, thereby ensuring the reliability of the data negotiation process.

[0038] In some embodiments, the data sharing method further comprises:

[0039] After receiving the authorization instruction from the data owner, the authorization instruction is sent to the data requester.

[0040] As a reliable third party, the server can facilitate data transfer during the negotiation process between data subjects. After the data owner provides authorization instructions, the server can forward them to the data requester. This completes the negotiation between the data requester and the data owner. The two parties can then begin exchanging data around the target private data based on the agreed-upon details to achieve the data requester's intended use of the target private data, enabling data exchange and collaboration between different data subjects.

[0041] In some embodiments, after receiving the authorization instruction, the data sharing method further includes:

[0042] Obtaining interactive information between the data owner and the data requester;

[0043] The interaction information is written into the smart contract used for value settlement between the data owner and the data requester.

[0044] The server acts as a reliable third party during the data exchange process, recording all the interaction information uploaded by the participating data owners and data requesters during the interaction. This interaction information provides a true description of the data exchange process. This interaction information can be incorporated into the smart contract, allowing the data owners and data requesters to settle the value of the data after the data exchange is completed. This smart contract can be used to make the settlement more accurate. Through this settlement, the data subject providing the data can obtain certain benefits, which can to some extent encourage all data subjects to provide high-quality data.

[0045] In some embodiments, after receiving the authorization instruction, the data sharing method further includes:

[0046] Determine the negotiation result information between the data owner and the data requester based on the data negotiation request and authorization instruction;

[0047] The negotiation result information is written into the smart contract used for value settlement between the data owner and the data requester.

[0048] The server acts as a reliable third party during client-side data exchange, recording the negotiation results between the data owner and the data requester before the data exchange begins. This negotiation result information serves as a constraint and framework for the interaction process. This negotiation result information can be written into a smart contract, allowing the data owner and the data requester to obtain an estimated settlement result based on the smart contract before the data exchange begins, and to conduct settlement promptly after the data exchange concludes. Through settlement, the data subject providing the data can obtain certain benefits, which can, to a certain extent, encourage all data subjects to provide high-quality data.

[0049] In a second aspect, the present application provides a data sharing method applied to a client, comprising:

[0050] Obtain data feature standard information from the server;

[0051] Generate data features of private data of a target product based on data feature standard information;

[0052] Upload data features to the server.

[0053] In a third aspect, the present application provides a data sharing system, including a server and multiple clients, wherein the server implements the data sharing method of the first aspect, and the client implements the data sharing method of the second aspect.

[0054] In a fourth aspect, the present application provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the method of the first aspect when executing the computer program; or, the processor implements the steps of the method of the second aspect when executing the computer program.

[0055] In a fifth aspect, the present application provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the steps of the method of the first aspect; or, when the computer program is executed by a processor, it implements the steps of the method of the second aspect.

[0056] It can be understood that the beneficial effects of the second to fifth aspects mentioned above can be found in the relevant description of the first aspect mentioned above, and will not be repeated here.

[0057] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the embodiments or descriptions of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0059] FIG1 is a schematic diagram of the architecture of a data sharing system provided in one embodiment of the present application;

[0060] FIG2 is a flow chart of a data sharing method provided in an embodiment of the present application;

[0061] FIG3 is a flow chart of a data sharing method according to an embodiment of the present application;

[0062] FIG4 is an exemplary diagram of the architecture of a data sharing method in an actual application scenario provided by an embodiment of the present application;

[0063] FIG5 is a schematic structural diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0064] In the following description, specific details such as specific system structures and techniques are provided for purposes of illustration rather than limitation to facilitate a thorough understanding of the embodiments of the present application. However, it will be apparent to those skilled in the art that the present application may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid obscuring the description of the present application with unnecessary detail.

[0065] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, integers, steps, operations, elements and / or components, but does not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or collections thereof.

[0066] It will also be understood that the term "and / or" used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

[0067] As used in this specification and the appended claims, the term "if" can be interpreted as "when" or "upon" or "in response to determining" or "in response to detecting," depending on the context. Similarly, the phrase "if it is determined" or "if [described condition or event] is detected" can be interpreted as meaning "upon determination" or "in response to determining" or "upon detection of [described condition or event]" or "in response to detecting [described condition or event]," depending on the context.

[0068] In addition, in the description of the present application specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0069] References to "one embodiment" or "some embodiments" in this specification mean that a particular feature, structure, or characteristic described in conjunction with that embodiment is included in one or more embodiments of the present application. Thus, phrases such as "in one embodiment," "in some embodiments," "in other embodiments," and "in yet other embodiments" appearing in various places in this specification do not necessarily refer to the same embodiment, but rather mean "one or more but not all embodiments," unless otherwise specifically emphasized. The terms "including," "comprising," "having," and variations thereof all mean "including but not limited to," unless otherwise specifically emphasized.

[0070] A product often involves multiple parties throughout its entire life cycle, from demand, planning, design, production, distribution, operation, use, repair and maintenance to recycling and disposal. These parties will act on the product in different ways, affecting the product's status. For example, batteries, as a product with complex mechanisms, involve multiple different parties throughout their life cycle, depending on the application field. Taking batteries used in vehicles as an example, the parties involved in such batteries may include but are not limited to: battery manufacturers, vehicle manufacturers, charging station operators and car owners. Taking batteries used in mobile phones as an example, the parties involved in such batteries may include but are not limited to: battery manufacturers, mobile phone manufacturers, charging accessories manufacturers, mobile phone users and shared power bank operators.

[0071] For various reasons, including commercial interests, data privacy, and policy restrictions, any participant in a product cannot know the identities of other participants, let alone the product-related data held by each participant. This leads to participants being unwilling or unable to directly exchange and share their product-related data, creating data barriers. These data barriers can hinder timely and accurate product optimization, reducing product competitiveness. For example, when considering battery state modeling for vehicles, if only data from a single participant is used during the modeling process, the limitations of this single data source may make it difficult for the resulting model to accurately estimate and predict various battery parameters, such as state of health (SOH), state of charge (SOC), and state of power (SOP). This consequence may affect subsequent battery safety testing, fault warning, health assessment, lifespan prediction, and optimal control, thereby limiting further commercial applications based on accurate battery state estimation, such as insurance assessments for used electric vehicles. If this continues, the development of batteries may be hindered, and the batteries currently in use may be difficult to optimize, resulting in reduced battery life or shortened lifespan of the batteries currently in use, and even battery-induced accidents may cause loss of life and property.

[0072] Based on the above considerations, in order to alleviate the data barriers between different participants and help promote data collaboration among different participants, the embodiments of the present application propose a data sharing method, a data sharing system, an electronic device and a storage medium, which can realize data sharing among various participants by collecting and sharing data features of the data obtained by each participant for the same type of product on the server side.

[0073] Because the server collects and shares data not directly from the participants, it can guarantee the security and privacy of each participant's data. Instead, the server collects and shares data characteristics, which are derived from the participants' data. These characteristics provide an identifiable overview of the data and, to a certain extent, represent that data. Therefore, each participant can roughly understand what data other participants possess, while ensuring data security and privacy.

[0074] The embodiment of the present application proposes a data sharing method that can be applied to the server. The server can be a single server; or, the server can be a server cluster composed of multiple servers, that is, the server can be a distributed server, and the embodiment of the present application does not limit the type of the server. Considering that the data sharing method proposed in the embodiment of the present application has certain requirements for data security and privacy protection, the credibility of the server can be considered when building the server. For example, the server can be a third-party server built by a credible organization or institution; or, the server can be a blockchain, and the embodiment of the present application does not limit this.

[0075] The present application also proposes a data sharing method applicable to a client. The client can be a web client, or a standalone client adapted to the aforementioned server, or integrated into a user data system. The present application does not limit the type of client.

[0076] The embodiment of the present application also provides a data sharing system, which includes a server and a client. Please refer to Figure 1, which shows a schematic diagram of the architecture of the data sharing system. Each client can establish a communication connection with the server according to its own needs, thereby realizing direct interaction between each client and the server; at the same time, indirect interaction between each client can also be realized through the server. The server in the data sharing system can execute the data sharing method applied to the server; the client in the data sharing system can execute the data sharing method applied to the client, which will not be described in detail here.

[0077] To facilitate understanding of the data sharing method applied to the server proposed in the embodiment of the present application, the data sharing method is explained and illustrated below through a specific embodiment. Referring to Figure 2, the data sharing method includes:

[0078] Step 201: Acquire data characteristics of private data of at least one data subject in the life cycle of a target product.

[0079] The target product may involve multiple data subjects throughout its entire life cycle, including production, manufacturing, use, secondary processing, and / or research. Each data subject may generate private data about the target product. The following explains and illustrates the various concepts involved in the above definition:

[0080] A target product is a pre-specified product category designed to minimize data barriers. Specifically, a target product is a product currently under consideration for breaking data barriers. For example, a target product could be a battery, and this embodiment of the present application does not limit the application areas of batteries.

[0081] Data subjects are all parties involved in the target product's life cycle, including direct and indirect participants. Taking batteries used in vehicles as an example, direct participants include but are not limited to battery manufacturers, vehicle manufacturers, charging station operators, and vehicle owners. Indirect participants include but are not limited to research teams and battery service providers that use batteries as research objects. It is understood that any individual, organization, or institution that can generate data related to batteries (including direct and indirect data) can be considered a data subject.

[0082] Data generated by a data subject related to a target product, that is, data generated by a data subject related to a target product, is the data subject's private data related to the target product. It should be understood that the term "private" in private data simply indicates that the data is valuable data obtained by the data subject related to the target product and is not widely disclosed. Generally speaking, private data has unique data content, meaning that the data content of private data held by different data subjects related to a target product will typically vary. Furthermore, private data can have diverse data types, specifically related to the data subject providing the private data. For example, for a battery used in a vehicle, the private data of a research team using batteries as a research object could include battery model data. The private data of a battery service provider could include battery service data. The private data generated by a vehicle owner could include battery usage data. The private data generated by a battery manufacturer could include battery design data and / or production data. These are not discussed in detail here.

[0083] Because private data is not widely disclosed and has certain value, to protect the privacy and security of the private data, corresponding data features can be generated based on the private data. The data features can be generated by the server or the client, and this embodiment of the application does not limit this.

[0084] As can be understood, data features are generated based on private data, making them objective summaries of private data. Furthermore, based on the word "feature," it's clear that they are primarily derived from information that is unique (or has certain differences) to the private data. Combining these two points, we can see that data features are objective, identifying summaries of private data and can be used to uniquely represent that private data.

[0085] When the server has established a communication connection with the client, the server can obtain the data characteristics of the private data of the target product corresponding to the data subject of the client through interaction with the client.

[0086] Step 202: Based on the association relationship between the data subject and the data features, the data features are integrated into the virtual data set.

[0087] Considering that different data subjects may impact a target product at different stages of its lifecycle, the server can pre-create a virtual dataset, which is initially empty. Whenever the server obtains a data feature of a data subject's private data, it can add the acquired data feature and its association with the data subject to the virtual dataset. Because data features are objective and identifiable summaries of private data and can be used to uniquely represent the corresponding private data, the above association effectively expresses the relationship between the private data, the data subject that owns the private data, and the data feature of the private data.

[0088] It is understood that as the server continues to operate, it may successively acquire data features of different private data related to the target product from multiple data subjects, and continuously update the virtual dataset based on the chronological order of acquisition, thereby gradually enriching the virtual dataset. Over time, the server may form a virtual dataset covering the entire lifecycle of the target product. This virtual dataset can be understood as a data directory for the target product. While it does not contain the private data itself, it may include the data features of the private data and the relationship between the data features and the data subjects.

[0089] Step 203: Share the virtual dataset.

[0090] The server can share data based on the virtual dataset. Specifically, the server can directly share the virtual dataset itself; of course, the server can also consider sharing only part of the content in the virtual dataset, such as the content within a specific time period, which will not be elaborated here. It should be noted that the embodiment of the present application does not limit the sharing timing of the data feature. For example, the server may obtain the data feature and update the virtual dataset based on the data feature, and then immediately share data based on the virtual dataset; or, it may also share data based on the virtual dataset at a specified time; or, it may also share data based on the virtual dataset in response to the needs of the data subject, and the embodiment of the present application does not limit this.

[0091] It can be understood that data features are objective and identifiable summaries of private data, uniquely representing that private data. Based on this, the virtual dataset includes not only the most recently received data features by the server, but also data features previously received by the server, and also includes the relationships between each data feature and the data subject. Therefore, based on the server's sharing of this virtual dataset, each data subject can, through their client system, comprehensively access the data features of each private data related to the target product, enabling each data subject to gain a preliminary and general understanding of the private data held by other data subjects.

[0092] In some embodiments, because the server's computing power is generally superior to that of the client and the server is a reliable third party, the client may consider uploading private data to the server. In this application scenario, step 201 may specifically involve: receiving private data from at least one data subject during the target product's lifecycle; and generating data features based on this private data. In other words, after obtaining the data subject's private data specific to the target product, the client can directly upload this private data to the server, which will generate data features for this private data and share them. This can reduce the processing pressure on the client.

[0093] In some embodiments, considering that the server can establish communication connections with multiple clients, if there are a large number of clients and data is uploaded concurrently, even if the server has strong computing power, its processing efficiency may be affected. In addition, although the server is a reliable third party, for data security reasons, the data subject may still want to store private data only locally on its client to minimize the possibility of data leakage. In this case, step 201 can be specifically manifested as: receiving data features of the private data of at least one data subject in the life cycle of the target product. That is, after obtaining the data subject's private data for the target product, the client will first generate data features based on the private data locally, and then only upload the data features to the server, so that the private data remains unknown to the server. In this way, the processing pressure on the server when multiple clients upload data concurrently can be reduced, and the data privacy and security of each data subject can be guaranteed.

[0094] In some embodiments, different data subjects may have an effect on the target product at different stages of the target product's life cycle, which may result in different data expressions for the same data object by each data subject, which may be specifically reflected in different data units or different data magnitudes. For example, when describing voltage, some data subjects may describe it in volts, while other data subjects may describe it in millivolts. This may cause data subjects to misunderstand the private data of other data subjects, resulting in the subsequent data exchange and collaboration between data subjects being affected. Based on this, when the client uploads private data to the server and the server needs to generate data features for the private data, it may be considered to introduce data standardization operations on the server. Then, the steps for the server to generate data features based on private data may include:

[0095] A1. Based on the preset data feature standard information, the private data is standardized to obtain standardized data.

[0096] The server can pre-set a unified data feature standard information. The data feature standard information provides the data standards such as the data unit and data magnitude expected by each data object related to the target product when sharing data, which is not limited here. Therefore, after receiving the private data, the server can standardize the private data based on the data feature standard information, and the result is the standardized data. It can be understood that the standardized data must comply with the data standard indicated by the data feature standard information. Taking the target product as a battery used in a vehicle as an example, the data feature standard information can be set based on the unified standard of the national standard GBT 32960.3-2016-Technical Specification for Electric Vehicle Remote Service and Management System, which will not be elaborated here.

[0097] In some examples, the server can develop a standardized interface based on the preset data feature standard information. In this way, the server can directly call the standardized interface to process the received private data to obtain standardized data, which can improve the efficiency of the server's standardized processing to a certain extent.

[0098] A2. Generate data features based on standardized data.

[0099] After obtaining the standardized data, the server may no longer generate data features directly based on private data, but may generate the data features based on the standardized data; that is, after the data features are generated, the server will perform subsequent processing based on the standardized data.

[0100] Through data standardization, different representations of the same data object across various data subjects are unified. This reduces misunderstandings between data subjects regarding other data subjects' private data and, to a certain extent, facilitates subsequent data exchange and collaboration between data subjects. Furthermore, this data standardization is performed by the server, which possesses higher computing power than the client, reducing the processing pressure on each client.

[0101] In some embodiments, invalid and / or redundant data may appear in the private data generated by the data subject for the target product. This may be caused by anomalies of the target product or by anomalies in the data collection process. These invalid and / or redundant data will affect the quality and value of the private data, and will also affect the efficiency of data processing. Based on this, when the client uploads private data to the server and the server needs to generate data features of the private data, before step A2, the server may also perform data cleaning on the received private data to obtain cleaned data. As an example only, the data cleaning process includes but is not limited to: filling (or removing) blank data, removing obviously abnormal data, and removing duplicate data, etc., which are not limited here.

[0102] It is understood that, based on this data cleansing process, step A2 can be optimized to: standardize the cleansed data based on preset data feature standard information to obtain standardized data. That is, after obtaining the cleansed data, the object of standardization is no longer private data, but rather the cleansed data. Obviously, the volume of this cleansed data must not be greater than the original volume of private data, which can improve the efficiency of the standardization process to a certain extent. Furthermore, the resulting standardized data will inevitably be free of invalid and / or redundant information, thereby improving data quality and value. Furthermore, this data cleansing process is implemented by the server, which has higher computing power than the client, reducing the processing pressure on each client.

[0103] In some embodiments, in order to conveniently verify the integrity and reliability of private data and prevent the data subject from privately tampering with the private data when sharing data through the server, the data characteristics of the private data obtained by the server may include: a summary of the private data.

[0104] The digest proposed in the embodiments of the present application specifically refers to a pseudo-random value of fixed length obtained by processing private data according to a given digest algorithm. It is understood that, for a particular private data, as long as the private data has not been tampered with, the result obtained by processing the private data with the same digest algorithm at any time is generally the same; conversely, if the private data has been tampered with, the results obtained by processing the private data with the same digest algorithm before and after the tampering are generally different.

[0105] As an example only, the given digest algorithm may be: Secure Hash Algorithm (SHA), Message Digest Algorithm (Message Digest Algorithm) and variations of any of the above algorithms, which is not limited in the embodiments of the present application.

[0106] In some embodiments, to facilitate each data subject to have a general understanding of the private data held by other data subjects and to help promote subsequent data exchange and collaboration between data subjects, the data characteristics of the private data obtained by the server may include: a data description of the private data.

[0107] Among them, the data description proposed in the embodiment of the present application specifically refers to: attribute information of private data, and the information type of the attribute information includes but is not limited to one or more of the following: data subject, data type, data size, data time range, data quality assessment and data value assessment, etc. Specifically, the data subject has been described in the previous article, which refers to the individual, organization or institution that owns the private data in a legal sense; data quality assessment refers to the assessment result of the data quality of the private data, which can be obtained by a reliable data quality assessment agency evaluating the private data; data value assessment refers to the assessment result of the data value of the private data, which can be obtained by a reliable data value assessment agency evaluating the private data.

[0108] For any data subject, the type of information that should be included in the data description obtained by the server based on the data subject can be determined by the data subject independently; or, it can be determined by the data subject through negotiation between its client and the server, which is not limited here.

[0109] Considering that the types of information contained in data descriptions obtained from different data subjects may vary, the server can pre-set a unified data description format. Subsequent data descriptions of private data obtained by the server must conform to this data description format, facilitating unified processing by the server and easy access by all data subjects.

[0110] In some embodiments, as described above, the server can establish communication connections with multiple clients. Different data subjects can interact with the server through their respective clients, allowing the server to share virtual datasets. In one application scenario, to reduce the interaction burden between the server and the client, step 203 can be specifically implemented through the following process:

[0111] When a data search request is received from a data search party, a virtual dataset is sent to the data search party based on the data search request.

[0112] When no requests are received, the server can be in an interactive waiting state. Once the server receives a data access request initiated by a client, it can confirm that the data subject corresponding to the client has a need to access the virtual dataset, and the client is the data access party. In this case, the server can determine that it is necessary to share the virtual dataset with the data access party. Specifically, the server can determine the specific client that is the data access party based on the address sent by the data access request, and send the virtual dataset to the data access party, so that the data access party can display the virtual dataset on its display interface for the data subject corresponding to the data access party to access the virtual dataset.

[0113] That is, in this application scenario, the server does not actively share the virtual data set, but can passively share it according to the needs of the data query party (that is, the needs of the data subject corresponding to the data query party). This can reduce the situation of pushing virtual data sets to the same client too frequently, and can alleviate the interaction burden between the server and the client.

[0114] In another application scenario, in order to reduce the situation where the client is unaware of the update of the virtual dataset, step 203 can also be specifically implemented through the following process:

[0115] Send virtual data sets to data review parties at a preset specified time point.

[0116] Before the designated time point is reached, the server can be in an interactive waiting state. Once the designated time point is reached, the server can send the latest virtual dataset at that time to the data query party, so that even if the data subject fails to initiate a data query request through its client, it can still query the latest virtual dataset in a timely manner. Among them, the data query party can be all clients; or, the data query party can also be the client corresponding to the data subject who has certain requirements for the real-time performance of the virtual dataset, which is not limited here. The designated time point can be set by the server; or, the designated time point can also be set by the data subject who has certain requirements for the real-time performance of the virtual dataset through the corresponding client, for example, setting multiple designated time points based on a certain time interval, which is not limited here.

[0117] That is, in this application scenario, the server can proactively share virtual datasets with data searchers at pre-defined time points. Since there's typically a certain time interval between adjacent designated time points, this reduces the risk of overly frequent pushes of virtual datasets to the same client, easing the interaction burden between the server and client. Furthermore, when a virtual dataset is updated, the server can proactively share it at a specific time point, eliminating the need for the data searcher to initiate a data search request. This reduces the risk of clients being unaware of updates to the virtual dataset.

[0118] In another application scenario, considering that the data subject may be interested in only some specific data, step 203 may also be implemented through the following process:

[0119] When the currently received data feature is a data feature that the data query party is interested in, the virtual data set is sent to the data query party.

[0120] Each data subject can, based on their needs, inform the server through their client which specific data features they are interested in. When the data features currently captured by the server meet the needs of a data subject, the client corresponding to that data subject is designated as the data searcher. The server can then send the virtual dataset to the searcher, allowing the corresponding data subject to promptly access new content in the virtual dataset, specifically the data features currently captured by the server and their corresponding relationship with private data.

[0121] That is, in this application scenario, the server can realize timely and proactive sharing of virtual data sets at targeted and personalized sharing times based on the needs of the data subject.

[0122] In some embodiments, there is no direct communication connection between the clients, but only an indirect communication connection is established based on the server. In order to enable data exchange and collaboration between different data subjects and maximize the use of shared data, the data sharing method proposed in the embodiment of the present application may also include the following steps:

[0123] B1. Receive a data negotiation request sent by a data requester.

[0124] Each client can access the data characteristics of each private data based on the data sharing conducted by the server. Based on the description above, it can be seen that after each data subject has accessed the data characteristics of each private data through its client, it has a preliminary and general understanding of the private data owned by each data subject. Each data subject can thus make a preliminary judgment on whether other data subjects have the private data they need. For any data subject, if the data subject determines that there is private data that it needs based on the data characteristics of the private data shared by the server, it can initiate a data negotiation request for the private data it needs through its client to request the private data.

[0125] For ease of distinction, in the embodiment of the present application, the client that initiates the data negotiation request is recorded as the data requester, and the private data requested by the data requester is recorded as the target private data.

[0126] B2. Determine the data owner of the target private data.

[0127] From a legal perspective, the target private data does not belong to the server. Because the virtual dataset is similar to a data catalog for the target product, containing the data characteristics of each private data item and the relationship between these characteristics and the data subject, the server can use this virtual dataset to determine the data owner of the target private data, namely, the client corresponding to the data subject that owns the target data.

[0128] B3. Send a data negotiation request to the data owner.

[0129] The server can act as a bridge for data interaction, forwarding the data negotiation request sent by the data requester to the corresponding data owner, so that the data subject corresponding to the data owner can approve the data negotiation request.

[0130] Of course, the data negotiation process between the data requester and the data owner can also be carried out through other means. For example, the data requester and the data owner can negotiate directly offline without relying on the server. The negotiation process is not limited here.

[0131] In some embodiments, the data subject corresponding to the data owner may consider the data negotiation request initiated by the data requester and provide feedback based on the consideration result. For the server, it may face the following two different scenarios:

[0132] In the first scenario, the data subject corresponding to the data owner agrees to the data negotiation request. In this scenario, the data owner can provide an authorization instruction. This authorization instruction can first be fed back to the server, and then forwarded by the server to the data requester, so that the data subject corresponding to the data requester knows that the data subject corresponding to the data owner has agreed to the data negotiation request. Subsequently, the data requester and the data owner can exchange data for the target private data. In one example, the data requester and the data owner can specifically use privacy computing technology to exchange data, thereby protecting the data security of all data subjects.

[0133] The second scenario is that the data subject corresponding to the data owner rejects the data negotiation request. In this scenario, the data owner can provide a rejection instruction. This rejection instruction can also be fed back to the server, which then forwards it to the data requester, so that the data subject corresponding to the data requester is aware that the data subject corresponding to the data owner has rejected the data negotiation request.

[0134] It is understood that in actual applications, a data subject may have data requirements for multiple other data subjects, or may have multiple data requirements for a single other data subject. Regardless of the data subject's requirements, they can refer to the above process and initiate corresponding data negotiation requests to multiple other clients through their client. In other words, when a data requester initiates a data negotiation request, it may correspond to multiple data owners.

[0135] As an example only, a data negotiation request may include, but is not limited to, one or more of the following information: the requested data, the purpose of the data, the desired calculations based on the data, and the data pricing model. As a reliable third party, the server can record the data negotiation request and use it as evidence in the event of a subsequent data interaction dispute.

[0136] In some embodiments, after receiving the authorization instruction, the server can know that the data requester and the data owner are about to start data exchange for the target private data. Considering that private data has a certain value, this data exchange is usually not free. Based on this, the data sharing method may also include:

[0137] C1. Obtain the interaction information between the data owner and the data requester.

[0138] During the data interaction process between the data owner and the data requester, although the specific results of the interaction between the two parties will not be uploaded to the server, the interaction information of each data interaction operation actually performed by the two parties will be uploaded to the server. The server can thus record all interaction information involved in the entire data interaction process between the data owner and the data requester (including interaction operation record information and / or interaction operation summary information). This record can be a plaintext record or a non-plaintext record (such as a hash value). It can be understood that since the server is a reliable third party, the interaction information cannot be tampered with, denied, or forged.

[0139] C2. Write the interaction information into the smart contract used for value settlement between the data owner and the data requester.

[0140] The server is pre-configured with a smart contract negotiated between the data owner and the data requester. To ensure accurate value settlement, the interaction information between the data owner and the data requester can be written into the smart contract. This allows the smart contract to be aware of every interaction performed during the data exchange process, thereby determining the actual usage of the interacted data. After the data exchange is completed, the smart contract can be automatically triggered to perform value settlement, which is generally achieved through tokens or points, thereby realizing the transfer of value between the participating data entities.

[0141] In some embodiments, after receiving the authorization instruction, the server can know that the data requester and the data owner are about to start data exchange. Considering that data has a certain value, this data exchange is usually not free. Based on this, the data sharing method may also include:

[0142] D1. Determine the negotiation result information between the data owner and the data requester based on the data negotiation request and authorization instruction.

[0143] As previously described, a data negotiation request may include, but is not limited to, one or more of the following information: the requested data, the intended purpose of the data, the desired calculations based on the data, and the data pricing model. The authorization instruction indicates that the details of the data negotiation request have been confirmed by the data owner. Given that the data negotiation request primarily expresses expectations for subsequent interactions, the server can determine the negotiation results between the data owner and the data requester.

[0144] D2. Write the negotiation result information into the smart contract used for value settlement between the data owner and the data requester.

[0145] The server is pre-configured with a smart contract negotiated between the data owner and the data requester. Using this negotiated information, the server can estimate the expected usage of the data exchanged between the data owner and the data requester during the data exchange process. Generally speaking, the actual interaction process will not deviate significantly from this negotiated information, so this information can be incorporated into the smart contract, allowing the smart contract to estimate the value based on the expected data usage. After the interaction is completed, the smart contract can be automatically triggered to settle the value, which is typically achieved through tokens or points, thereby transferring value between the participating data entities.

[0146] In some embodiments, the server may support multiple transaction modes, as long as the data requester and the data participants have negotiated and determined them in advance. As an example only, the data pricing modes supported by the server may include but are not limited to: automatic price matching mode, dynamic price adjustment mode based on transaction records, and customized modes by the parties involved in the interaction, etc., which are not limited here. In the case where the data negotiation request carries a data pricing mode, the server may configure the data pricing mode of the smart contract based on the negotiation results after the data negotiation request is authorized; or, the data pricing mode may be determined by other means, for example, the data owner has indicated the data pricing mode it wants to adopt on the server. If there is still a data requester initiating a negotiation with the data owner, it is assumed that the data subject corresponding to the data requester has agreed to the data pricing mode.

[0147] In some embodiments, a supervisory terminal may also be provided to supervise the various information recorded by the server. As an example only, in the case where the server is a blockchain, the supervisory terminal may exist as a blockchain node. Since the data stored in the blockchain is tamper-proof, non-forgeable, and non-repudiable, supervisory agencies (such as government agencies or government-designated agencies) can view all current records of the blockchain through the supervisory terminal. In addition, the supervisory agency may also require any data subject to provide various operation records and / or private data through its client through its supervisory terminal according to regulatory needs, and compare the operation records and / or private data provided by the data subject with the existing records on the blockchain to verify the data and / or operations, thereby achieving convenient, friendly, and reliable supervision and auditing.

[0148] To facilitate understanding of the data sharing method applied to the client proposed in the embodiment of the present application, the data sharing method is explained and illustrated below through a specific embodiment. Referring to Figure 3, the data sharing method includes:

[0149] Step 301: Obtain private data of the data subject corresponding to the client for the target product.

[0150] After collecting the private data for the target product, the data subject can input the private data into the client. The concepts of data subject, target product and private data have been explained in other embodiments proposed above and will not be repeated here.

[0151] It's important to note that in actual application scenarios, the data subject may not have the ability to collect private data themselves. In such scenarios, the data subject can entrust a third party with data collection capabilities to collect private data specific to the target product and input this private data into the client. In other words, the data subject doesn't necessarily have to be responsible for collecting the private data specific to the target product.

[0152] Step 302: Upload data to the server based on the private data.

[0153] The client can periodically upload private data obtained in the most recent cycle; alternatively, the client can, after receiving an upload instruction from the data subject, then upload private data that has not yet been uploaded. The timing of the upload is not limited here. The purpose of this upload process is to enable the server to obtain and share the data characteristics of the private data. These data characteristics have been described in other embodiments previously presented and will not be repeated here.

[0154] It can be understood that since data features are an identifiable and objective overview of private data and can be used to uniquely represent the private data, the sharing operation performed by the server after the client uploads the data can enable other data subjects to have a preliminary and general understanding of the private data.

[0155] In some embodiments, because the server's computing power is generally superior to that of the client and the server is a reliable third party, step 302 may specifically involve uploading the private data to the server. That is, after obtaining the private data, the client can directly upload it to the server, which then generates data features for the private data and shares them. This reduces the processing pressure on the client.

[0156] In some embodiments, considering that the server can establish communication connections with multiple clients, if there are a large number of clients and data is uploaded concurrently, even if the server has strong computing power, its processing efficiency may be affected. In addition, although the server is a reliable third party, for data security reasons, the data subject may still want to store private data only locally to minimize the possibility of data leakage. Therefore, step 302 can be specifically expressed as: generating data features based on the private data; uploading the data features to the server. That is, after obtaining the private data, the client will first generate data features based on the private data locally, and then only upload the data features to the server, so that the private data remains unknown to the server. In this way, the processing pressure on the server when multiple clients upload data concurrently can be reduced, and the data privacy and security of each data subject can be guaranteed.

[0157] In some embodiments, different data subjects may play a role at different stages of the life cycle of the target product, which may result in different data expressions for the same data object by each data subject, which may be specifically reflected in different data units or different data magnitudes, etc. For example, when describing voltage, some data subjects may describe it in volts, while other data subjects may describe it in millivolts. This may cause data subjects to misunderstand the private data of other data subjects, resulting in the subsequent data exchange and collaboration between data subjects being affected. Based on this, in an embodiment of the present application, when the data features are generated by the client, the client may consider introducing data standardization operations, and the steps for generating data features based on private data may be specifically as follows:

[0158] E1, obtain the data feature standard information of the server.

[0159] After establishing a communication connection with the server, the client can first obtain data characteristic standard information pre-set by the server from the server. The data characteristic standard information has been described in other embodiments proposed above and will not be repeated here.

[0160] E2, generates data features of private data of the target product based on data feature standard information.

[0161] The client can generate data features of the private data of the target product based on the data feature standard information. It can be understood that the data features are an objective summary of the corresponding private data and can be used to uniquely represent the private data.

[0162] In some embodiments, step 302 may specifically include:

[0163] F1, based on the data feature standard information, standardizes the private data to obtain standardized data.

[0164] The client can perform standardization processing on the private data based on the obtained data characteristic standard information to obtain standardized data. It is understood that the standardized data must conform to the data standard indicated by the data characteristic standard information.

[0165] In some examples, the server can develop a standardized interface based on preset data feature standard information. In this way, the client can directly call the standardized interface to process private data and obtain standardized data, which can improve the efficiency of the client's standardized processing to a certain extent.

[0166] F2, generates data features based on standardized data.

[0167] After obtaining the standardized data, the client may no longer directly generate data features based on private data, but may generate the data features based on the standardized data; that is, after the data features are generated, subsequent processing will be performed based on the standardized data.

[0168] It can be understood that through data standardization processing, the different expressions of the same data object by each data subject are unified, which can reduce the situation where data subjects have misunderstandings about the private data of other data subjects, and to a certain extent promote subsequent data exchange and collaboration between data subjects.

[0169] In some embodiments, invalid and / or redundant data may appear in the private data generated by the data subject for the target product. This may be caused by anomalies of the target product or by anomalies in the data collection process. These invalid and / or redundant data will affect the quality and value of the private data, and will also affect the efficiency of data processing. Based on this, in an embodiment of the present application, when the data features are generated by the client, it may be considered to introduce a data cleaning operation before performing the data standardization operation (that is, before step F1), specifically: performing data cleaning processing on the private data to obtain cleaned data.

[0170] As an example only, the data cleaning process includes but is not limited to: filling (or removing) blank data, removing obviously abnormal data, and removing duplicate data, etc., which are not limited here.

[0171] Accordingly, the data standardization operation performed by the client can be optimized to: Based on the data feature standard information, the cleansed data is normalized to obtain standardized data. That is, after obtaining the cleansed data, the client no longer performs standardization on the original private data, but on the cleansed data. Obviously, the volume of the cleansed data is necessarily no larger than the original private data, which can improve the efficiency of the standardization process to a certain extent. Furthermore, the obtained standardized data is also guaranteed to be free of invalid and / or redundant information, thereby improving data quality and value.

[0172] In some embodiments, to help promote subsequent data exchange and collaboration between data subjects, the data characteristics of private data may include a data description of the private data, which can be specifically generated by the client through the following steps: generating a data description based on a preset data description format and the private data obtained by the client.

[0173] Among them, the data description has been explained in the other embodiments proposed above and will not be repeated here. Taking into account that the types of information contained in the data descriptions obtained based on different data subjects may be different, the server can pre-set a unified data description format and send the unified data description format to each client. In this way, the data descriptions generated by each client have the same data description format, which is convenient for the server to process uniformly and for each data subject to consult. Of course, after the client performs standardization based on private data and obtains standardized data, the client can also generate data descriptions based on the data description format and the standardized data, which will not be repeated here.

[0174] In some embodiments, in order to conveniently implement integrity and reliability verification of private data and prevent the data subject from privately tampering with their private data when sharing data through the server, the data characteristics of the private data may include a summary of the private data, which can be specifically generated by the client through the following steps: generating a summary based on a preset summary algorithm and the private data obtained by the client.

[0175] Among them, the summary has been explained in the other embodiments proposed above and will not be repeated here. The summary algorithm adopted by the client here can be pre-set by the server, so that the summary algorithms adopted by each client can remain unified, avoiding data confusion caused by different clients using different summary algorithms. As an example only, the summary algorithm can be: a secure hash algorithm, an information digest algorithm and a variant of any of the above algorithms, which is not limited in the embodiments of the present application. Of course, after the client performs standardization based on private data and obtains standardized data, the client can also generate a summary based on the summary algorithm and the standardized data, which will not be repeated here.

[0176] In some embodiments, after obtaining the data features of the client's private data, the server can share the data features by sharing the virtual data set. During this process, if other data subjects are interested in the private data owned by the client, the other clients (that is, the clients corresponding to the other data subjects) can initiate a data negotiation request for the private data to the server, and the server will forward the data negotiation request. Alternatively, if the other client has obtained the communication address of the client, the other client can also send a data negotiation request directly to the client. For ease of distinction, in an embodiment of the present application, the data negotiation request is recorded as a first data negotiation request, and the private data requested by the data negotiation request is recorded as the first target private data. It can be understood that in this case, the client plays the role of the data owner, and the other client plays the role of the data requester. Then, the client as the data owner can perform the following operations:

[0177] G1. Receive a first data negotiation request from a data requester to obtain first target private data.

[0178] The data owner may receive and parse the first data negotiation request. Since the first data negotiation request may include, but is not limited to, one or more of the following information: the requested data, the purpose of the data, the desired calculations based on the data, and the data pricing model, the data owner can understand the purpose of the request by parsing the first data negotiation request and display the purpose of the request to the data subject corresponding to the data owner.

[0179] G2. If a confirmation instruction input by the data subject is received, a first authorization instruction is generated to trigger data interaction based on the first target private data.

[0180] The data subject corresponding to the data owner can determine whether to agree to the first data negotiation request based on their own circumstances. If the data subject agrees, the data subject can enter a confirmation instruction, thereby causing the data owner to generate an authorization instruction for the first data negotiation request based on the confirmation instruction. For ease of distinction, this authorization instruction is referred to as the first authorization instruction in the embodiments of this application.

[0181] G3. Send a first authorization instruction to the data requester.

[0182] Based on the method of receiving the first data negotiation request, the data owner can determine the method for sending the first authorization instruction and send the first authorization instruction to the data requester. It is understood that if the data requester sends the first data negotiation instruction to the data owner through the server, the data owner can also send the first authorization instruction to the data requester through the server. If the data requester sends the first data negotiation instruction directly to the data owner, the data owner can also send the first authorization instruction directly to the data requester. After the data owner and the data requester are both prepared, the data owner and the data requester can establish a data interaction link to achieve data interaction based on the first target private data.

[0183] In some embodiments, because the server can establish communication connections with multiple clients, the server can actually store data features of multiple clients' private data. Based on this, any data subject can access the server through its corresponding client to view the data features stored by the server. It can be understood that in this case, the client plays the role of the data viewer, and as a data viewer, the client can perform the following operations:

[0184] H1. Send a data query request to the server.

[0185] Upon receiving a data subject's input, the data search party can send a data search request to the server. This data search request requests the server to share all stored data features, i.e., a virtual dataset. The virtual dataset has been described in other embodiments above and includes data features of multiple data subjects' private data related to the target product, so it will not be further described here.

[0186] H2. Receive a virtual data set including data features of different private data of the target product sent by the server.

[0187] Once the server receives a data access request from a data access party, it can confirm that the data access party has a need to access the virtual dataset, indicating that it is currently necessary to share the virtual dataset with the data access party. The server can then accurately send the virtual dataset to the data access party based on the address sent in the data access request, allowing the data access party to display the virtual dataset on its display interface for the data subject corresponding to the data access party to access the virtual dataset.

[0188] In some embodiments, after step H2, the data subject can consult the virtual data set through its client to learn the data characteristics of the private data owned by other data subjects, thereby forming a preliminary understanding of the private data owned by other data subjects. If the data subject determines that the private data owned by some other data subjects is the data he needs based on the virtual data set, he can initiate a data negotiation request for the private data to the corresponding other clients through the client to request the private data. The information carried by the data negotiation will not be repeated here. For ease of distinction, in the embodiment of the present application, the data negotiation request is recorded as the second data negotiation request, and the private data requested by the second data negotiation request is recorded as the second target private data. It can be understood that in the embodiment of the present application, the client plays the role of the data requester, and the other client plays the role of the data owner. Then the client can perform the following operations:

[0189] I1. In response to the interactive instruction input by the data subject, a second data negotiation request is sent to the data owner.

[0190] The data subject's interaction instruction expresses the data subject's demand for the second target private data. Upon receiving the data subject's interaction instruction, the data requester may, in response to the instruction, send a second data negotiation request to the data owner. Specifically, the data requester may send the second data negotiation request to the server, which then forwards the request to the data owner. Alternatively, if the data requester knows the data owner's communication address, the data requester may send the second data negotiation request directly to the data owner.

[0191] I2. If a second authorization instruction is received from the data owner, the second target private data is exchanged with the data owner.

[0192] The data subject corresponding to the data owner can determine whether to agree to the second data negotiation request based on their own circumstances. If the data subject agrees, the data subject can enter a confirmation instruction, which will cause the data owner to generate an authorization instruction for the second data negotiation request based on the confirmation instruction and send the authorization instruction to the data requester. This will not be described in detail here. For ease of distinction, this authorization instruction is referred to as the second authorization instruction in the embodiments of this application.

[0193] After receiving the second authorization instruction, the data requester can confirm that the data owner has agreed to data exchange with it. After the data requester and the data owner are both ready, the data requester and the data owner can establish a data exchange link to realize data exchange based on the second target private data.

[0194] It should be noted that in actual application scenarios, a client can simultaneously interact with one or more other clients. That is, when a client acts as a data requester, the second data negotiation instruction it initiates can simultaneously designate multiple other clients as data owners. Similarly, when a client acts as a data owner, it can simultaneously respond to first data negotiation requests initiated by multiple other clients acting as data requesters. In other words, the embodiments of this application do not limit the number of parties involved in data interaction.

[0195] In some embodiments, whether the client is acting as a data requester or as a data owner, the data interaction between it and any other client can be implemented based on privacy computing technology, and the type of privacy computing technology can be determined by negotiation between the client and the other client, that is, by negotiation between the data subject corresponding to the client and the data subject corresponding to the other client. Among them, the number of any other clients can be more than one, which can be determined by the specific data requirements of the client, and this application does not limit this. Through privacy computing technology, multi-party data can be efficiently utilized for fusion computing under the premise of compliance and ensuring the data security and privacy of all parties. It can be understood that the data interaction can be the direct transmission of private data (or direct transmission of standardized data), or the transmission of encrypted private data (or the transmission of encrypted standardized data), or the transmission of calculation results obtained based on private data and a given data calculation algorithm (or the transmission of calculation results obtained based on standardized data and a given data calculation algorithm). It can be specifically determined according to the type of privacy computing technology determined by negotiation, which is not limited here.

[0196] As an example only, the types of privacy computing technologies that can be adopted by the client include: secure multi-party computation (MPC), federated learning (FL), trusted execution environment (TEE), homomorphic encryption (HE) and differential privacy (DP), etc., which are not limited to the embodiments of this application.

[0197] In some embodiments, in order to achieve full-process supervision of data exchange and collaboration between clients by the server, during the process of data interaction between clients, each client can generate interaction information based on each data interaction operation that has been performed, and upload the interaction information to the server. Since the server is a reliable third party, the interaction information cannot be tampered with, denied or forged. If a dispute arises between the parties involved in the subsequent interaction, the dispute can also be resolved by consulting the various interaction information stored by the server during the interaction process. In addition, after the interaction has ended, the server can also obtain the actual usage of the interacted data based on the various interaction information uploaded by each client during the interaction process, and make accurate value settlement based on this.

[0198] To facilitate understanding of the data sharing method applied to the server, the data sharing method applied to the client, and the data sharing system formed by the server and the client, the following briefly summarizes the entire data sharing process, taking a battery used in a vehicle as an example as the target product:

[0199] Please refer to Figure 4, which shows an example of the data sharing system. The data subjects shown in Figure 4 include: battery manufacturers, vehicle manufacturers, charging station operators, vehicle owners, and battery recycling processors.

[0200] It can be understood that although Figure 4 shows only one data subject of each type, in actual applications, there can be multiple data subjects of each type, such as multiple battery manufacturers, multiple vehicle manufacturers, etc.; and the types of data subjects are not limited to the types shown in Figure 4. In actual applications, there can also be various types of battery-related data subjects such as university research teams on batteries and insurance service providers, which will not be elaborated here.

[0201] Each data subject can correspond to a client, which can upload the data characteristics of the private data obtained by the corresponding data subject for the battery to the corresponding blockchain node. For example, client 1 can upload the data characteristics of the private data obtained by the battery manufacturer for the battery to node 1, and client 2 can upload the data characteristics of the private data obtained by the vehicle manufacturer for the battery to node 2, etc., which will not be repeated here.

[0202] Each node in the blockchain can communicate the data characteristics of each received private data to each other, aggregating the data characteristics of the private data obtained by each data subject for the battery. By including data subjects at all stages of the battery's life cycle, the blockchain can ultimately produce a virtual dataset for the entire battery life cycle.

[0203] Each data subject can access the blockchain through its own client and view the virtual data set to learn the data characteristics of other data subjects' private data, and thereby determine the data it wishes to use (that is, the data to be interacted with), as well as the data subject corresponding to the data owner of the data (that is, the other data subject who legally owns the data). Afterwards, the data subject can negotiate with the other data subject through its client regarding the data to be interacted with, and interact with the data after negotiation. After the data interaction is completed, value settlement can be carried out through the blockchain, so that the data subject providing the data can obtain certain benefits, which helps to promote each data subject to provide better quality data. It should be noted that the number of interaction participants is not limited, and data interaction can be achieved based on the privacy computing technology negotiated by the interaction participants.

[0204] For example, a university research team focused on battery technology is working on a battery state prediction model. Hoping to obtain rich training samples from multiple data sources, the team can collaborate with multiple data entities through a data sharing system. Based on the private data of these entities, they can optimize their battery state prediction model in multiple dimensions, resulting in a more effective, accurate, and robust battery state prediction model. In other words, based on a virtual dataset covering the entire life cycle, multiple data entities can achieve value sharing through data exchange and collaboration, enabling all data entities to develop well. For example, battery manufacturers can improve battery production processes and optimize battery design, vehicle manufacturers can achieve lower accident rates, electric vehicles used by car owners can have longer driving range and longer battery life, insurance companies can reduce accident claims and increase revenue, and universities and research institutions can provide better algorithms and achieve industrial transformation.

[0205] Corresponding to the data sharing method provided above, an embodiment of the present application further provides an electronic device. Referring to FIG. 5 , the electronic device 5 in the embodiment of the present application includes: a memory 501, one or more processors 502 (only one is shown in FIG. 5 ), and a computer program stored on the memory 501 and executable on the processor. The memory 501 is used to store software programs and units, and the processor 502 executes the software programs and units stored in the memory 501 to execute various functional applications and data processing to obtain resources corresponding to the aforementioned preset events.

[0206] In the case where the electronic device is the server, the processor 502 can implement the steps of the data sharing method applied to the server by running the above-mentioned computer program stored in the memory 501, which will not be repeated here.

[0207] In the case where the electronic device is a client, the processor 502 can implement the steps of the data sharing method applied to the client by running the above-mentioned computer program stored in the memory 501, which will not be repeated here.

[0208] It should be understood that in the embodiment of the present application, the processor 502 may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc.

[0209] The memory 501 may include a read-only memory and a random access memory, and provides instructions and data to the processor 502. A portion or all of the memory 501 may also include a non-volatile random access memory. For example, the memory 501 may also store device category information.

[0210] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant description of other embodiments.

[0211] Those skilled in the art will appreciate that the algorithmic steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of external device software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0212] In the embodiments provided in this application, it should be understood that the disclosed systems and methods can be implemented in other ways.

[0213] If the above-mentioned integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present application implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the associated hardware through a computer program. The above-mentioned computer program can be stored in a computer-readable storage medium, and the computer program, when executed by the processor, can implement the steps of the above-mentioned various method embodiments. Among them, the above-mentioned computer program includes computer program code, and the above-mentioned computer program code can be in source code form, object code form, executable file or some intermediate form, etc. The above-mentioned computer-readable storage medium may include: any entity or device that can carry the above-mentioned computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer-readable memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium, etc. It should be noted that the content contained in the above-mentioned computer-readable storage medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable storage media does not include electrical carrier signals and telecommunication signals.

[0214] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present application, and should all be included in the scope of protection of the present application.

Claims

1. A data sharing method, characterized in that: Applied to the server, the data sharing method includes: Acquire data characteristics of private data of at least one data subject in the life cycle of the target product; Integrate the data features into a virtual data set based on the association between the data subject and the data features; The virtual dataset is shared.

2. The data sharing method according to claim 1, characterized in that: The step of acquiring data characteristics of private data of at least one data subject in the life cycle of the target product includes: receiving private data of at least one of the data subjects in the life cycle of the target product; The data feature is generated according to the private data.

3. The data sharing method according to claim 2, characterized in that: The step of generating the data feature according to the private data comprises: Based on preset data feature standard information, the private data is standardized to obtain standardized data; The data features are generated according to the standardized data.

4. The data sharing method according to claim 3, characterized in that: Before the private data is standardized based on the preset data feature standard information to obtain the standardized data, the data sharing method further includes: Performing data cleaning on the private data to obtain cleaned data; Accordingly, the private data is standardized based on the preset data feature standard information to obtain standardized data, including: Based on the data feature standard information, the cleaned data is standardized to obtain the standardized data.

5. The data sharing method according to claim 1, characterized in that: The step of acquiring data characteristics of private data of at least one data subject in the life cycle of the target product includes: A data characteristic of private data of at least one of the data subjects in the life cycle of the target product is received.

6. The data sharing method according to any one of claims 1 to 5, characterized in that: The data characteristics include: a summary and / or a data description of the private data.

7. The data sharing method according to claim 1, characterized in that: The sharing of the virtual data set comprises: Receive data access requests sent by data access parties; The virtual data set is sent to the data search party based on the data search request.

8. The data sharing method according to any one of claims 1 to 7, characterized in that: The data sharing method further includes: Receive a data negotiation request from a data requester to obtain target private data; Determining the data owner of the target private data; The data negotiation request is sent to the data owner.

9. The data sharing method according to claim 8, characterized in that: The data sharing method further includes: After receiving the authorization instruction from the data owner, the authorization instruction is sent to the data requester.

10. The data sharing method according to claim 9, characterized in that: After receiving the authorization instruction from the data owner, the data sharing method further includes: Acquiring interaction information between the data owner and the data requester; The interaction information is written into a smart contract used for value settlement between the data owner and the data requester.

11. The data sharing method according to claim 9, characterized in that: After receiving the authorization instruction from the data owner, the data sharing method further includes: Determining negotiation result information between the data owner and the data requester according to the data negotiation request and the authorization instruction; The negotiation result information is written into a smart contract used for value settlement between the data owner and the data requester.

12. A data sharing method, characterized in that: Applied to the client, the data sharing method includes: Obtain private data of the data subject corresponding to the client for the target product; Data is uploaded to the server according to the private data.

13. The data sharing method according to claim 12, characterized in that: The uploading of data to the server according to the private data includes: The private data is uploaded to the server.

14. The data sharing method according to claim 12, characterized in that: The uploading of data to the server according to the private data includes: generating a data feature based on the private data; The data features are uploaded to the server.

15. The data sharing method according to claim 14, characterized in that: The generating of data features according to the private data comprises: Obtaining data characteristic standard information of the server; The data feature of the private data is generated based on the data feature standard information.

16. The data sharing method according to claim 15, characterized in that: The step of generating the data feature of the private data based on the data feature standard information includes: Based on the data characteristic standard information, the private data is standardized to obtain standardized data; The data features are generated according to the standardized data.

17. The data sharing method according to claim 16, characterized in that: Before the private data is standardized based on the data feature standard information to obtain standardized data, the data sharing method further includes: Performing data cleaning on the private data to obtain cleaned data; Accordingly, the standardizing the private data based on the data feature standard information to obtain standardized data includes: Based on the data feature standard information, the cleaned data is standardized to obtain the standardized data.

18. The data sharing method according to claim 16 or 17, characterized in that: The data feature includes a data description of the private data; and generating the data feature according to the standardized data includes: The data description is generated according to a preset data description format and the standardized data.

19. The data sharing method according to claim 16 or 17, characterized in that: The data feature includes a summary of the private data; and generating the data feature according to the standardized data includes: The summary is generated according to a preset summary algorithm and the standardized data.

20. The data sharing method according to any one of claims 12 to 19, characterized in that: The data sharing method further includes: receiving a first data negotiation request from a data requester to obtain first target private data; If a confirmation instruction input by the data subject is received, generating a first authorization instruction for triggering data interaction based on the first target private data; The first authorization instruction is sent to the data requester.

21. The data sharing method according to any one of claims 12 to 19, characterized in that: The data sharing method further includes: Sending a data access request to the server; A virtual data set including data features of different private data of a target product is received from the server.

22. The data sharing method according to claim 21, characterized in that: After receiving the virtual data set including data features of different private data of the target product sent by the server, the data sharing method further includes: In response to an interactive instruction input by the data subject, determining second target private data based on the virtual data set; Sending a second data negotiation request to the data owner of the second target private data; If a second authorization instruction sent by the data owner is received, data interaction is performed with the data owner based on the second target private data.

23. The data sharing method according to claim 20 or 22, characterized in that: The data interaction is implemented based on privacy computing technology, and the privacy computing technology is determined by negotiation among the parties involved in the data interaction.

24. The data sharing method according to claim 20 or 22, characterized in that: The data sharing method further includes: In the process of data interaction, generating interaction information according to each data interaction operation that has been performed; Upload the interaction information to the server.

25. A data sharing system, characterized in that: The data sharing system includes: a server and a client; wherein the server implements the data sharing method as described in any one of claims 1 to 11, and / or the client implements the data sharing method as described in any one of claims 12 to 24.

26. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the data sharing method as described in any one of claims 1 to 11 is implemented; or, when the processor executes the computer program, the data sharing method as described in any one of claims 12 to 24 is implemented.

27. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the data sharing method according to any one of claims 1 to 24 is implemented.

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